Recommendation systems are used to recommend items to users. For example, a web site can recommend an item such as a book, web article, movie, restaurant or other product or service in which a particular user might be interested. Recommendation systems analyze patterns of user behavior as well as features of the items to provide personalized recommendations based on a user's interests, an item's features or some combination thereof. A matrix of data can be developed over time with entries which indicate a particular user's interest in particular items based on explicit or implicit feedback. The matrix can be processed to estimate a user's interest in other items for which feedback has not been provided and a recommendation for one or more of the others items can thereby be made. However, such a matrix can become very large, such as when a population of millions of users is analyzed, so that the processing of the matrix consumes excessive time and computational resources.